{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# single_snp_scale: Ludicrious-Speed GWAS \r\n",
    "\r\n",
    "\r\n",
    "##### Version 0.6.0\r\n",
    "\r\n",
    "Carl Kadie & David Heckerman, July 21, 2021\r\n",
    "\r\n",
    "### Introduction\r\n",
    "\r\n",
    "As described in [Kadie & Heckerman, 2017](https://www.biorxiv.org/content/10.1101/154682v3), FaST-LMM's *single_snp_scale* function is a clusterable version of the FaST-LMM's\r\n",
    "[single_snp](http://fastlmm.github.io/FaST-LMM/#single-snp) function.\r\n",
    "\r\n",
    "The *single_snp_scale* function supports GWAS analysis on up to 1,000,000 individuals. It also runs fine on a single computer. This is useful for testing and for some runs a bit too  big for *single_snp*."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Comparing *single_snp* to *single_snp_scale*\r\n",
    "\r\n",
    "This table summarizes the differences between *single_snp* to *single_snp_scale*.\r\n",
    "\r\n",
    "\r\n",
    "| single_snp                 | single_snp_scale              |\r\n",
    "|----------------------------|-------------------------------|\r\n",
    "| runs on 1 machine          | runs on cluster or 1 machine  |\r\n",
    "| less overhead              | more overhead                 |\r\n",
    "| scales to ~10K individuals | scales to ~1M individuals     |\r\n",
    "| full or low_rank           | low rank only                 |\r\n",
    "| uses only main memory  | uses main memory and SSD      |\r\n",
    "| 0,1,2 kernels          | 1 kernel                      |\r\n",
    "| SNP kernel or explicit | SNP kernel                    |\r\n",
    "| caches some work       | caches all work               |\r\n",
    "| defaults to cross validate over chromosomes | always cross validates over chromosomes |\r\n",
    "| REML                   | REML                          |\r\n",
    "\r\n",
    "\r\n",
    "Within these constraints, both functions produce the same answers.\r\n",
    "\r\n",
    "The *single_snp_scale* function can run on any cluster, but such runs require these modules:\r\n",
    "* a [runner](http://fastlmm.github.io/PySnpTools/#module-pysnptools.util.mapreduce1) : tells how to programmatically run batch jobs on the cluster of interest\r\n",
    "* a [file_cache](http://fastlmm.github.io./PySnpTools/#module-pysnptools.util.filecache) : tells how to programmatically upload and download files to the nodes of the cluster.\r\n",
    "\r\n",
    "Finally, as described in [Kadie & Heckerman, 2019](https://www.biorxiv.org/content/10.1101/154682v3), the *single_snp_scale* functions requires multiple cluster runs are needed to find number of SNPs needed to measure the similarity between individuals.\r\n",
    "\r\n",
    "### Documentation\r\n",
    "\r\n",
    "Find all the inputs and options for *single_snp_scale* described in its\r\n",
    "[documentation](http://fastlmm.github.io/FaST-LMM/#single-snp-scale).\r\n",
    "\r\n",
    "\r\n",
    "See [FaST-LMM's README.md](https://github.com/fastlmm/FaST-LMM/blob/master/README.md) for installation instructions, more documentation, code, and a bibliography.\r\n",
    "\r\n",
    "To work with a kernel larger than about 16K, requires a NumPy library that can do eigenvalue decompositions larger than 16K, for example, one that uses MKL IPL64. Alternatively,\r\n",
    "use an older version of FaST-LMM (version 0.5.*) which includes its own version of MKL IPL64.\r\n",
    "\r\n",
    "\r\n",
    "### Contacts\r\n",
    "\r\n",
    "* Email the developers at fastlmm-dev@python.org.\r\n",
    "* [Join](mailto:fastlmm-user-join@python.org?subject=Subscribe) the user discussion and announcement list (or use [web sign up](https://mail.python.org/mailman3/lists/fastlmm-user.python.org)).\r\n",
    "* [Open an issue](https://github.com/fastlmm/FaST-LMM/issues) on GitHub."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### Notebook preparation and general use\n",
    "\n",
    "To prepare this notebook to run analyses, please run the following script."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# set some ipython notebook properties\r\n",
    "%matplotlib inline\r\n",
    "\r\n",
    "# set degree of verbosity (adapt to INFO for more verbose output)\r\n",
    "import logging\r\n",
    "logging.basicConfig(level=logging.WARNING)\r\n",
    "\r\n",
    "# set figure sizes\r\n",
    "import pylab\r\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\r\n",
    "\r\n",
    "# set display width for pandas data frames\r\n",
    "import pandas as pd\r\n",
    "pd.set_option('display.width', 1000)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "To introduce *single_snp_scale*, let's first run it on the *single_snp* example from the [FaST-LMM notebook](https://nbviewer.jupyter.org/github/fastlmm.github.io/FaST-LMM/blob/master/doc/ipynb/FaST-LMM.ipynb). On a 6-processor consumer PC, the run takes about 1.5 minutes."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "from fastlmm.association import single_snp_scale\r\n",
    "from fastlmm.util import example_file # Download and return local file name\r\n",
    "\r\n",
    "bed_fn = example_file('tests/datasets/synth/all.*','*.bed')\r\n",
    "pheno_fn = example_file(\"tests/datasets/synth/pheno_10_causals.txt\")\r\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\r\n",
    "\r\n",
    "results_df = single_snp_scale(bed_fn, pheno_fn, covar=cov_fn, count_A1=False)\r\n",
    "\r\n",
    "# manhattan plot\r\n",
    "import pylab\r\n",
    "import fastlmm.util.util as flutil\r\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\r\n",
    "flutil.manhattan_plot(results_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5,xaxis_unit_bp=False)\r\n",
    "pylab.show()\r\n",
    "\r\n",
    "# qq plot\r\n",
    "from fastlmm.util.stats import plotp\r\n",
    "plotp.qqplot(results_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\r\n",
    "\r\n",
    "# print head of results data frame\r\n",
    "import pandas as pd\r\n",
    "pd.set_option('display.width', 1000)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lambda=1.0078\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451117\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251925e-23  -0.416495     0.040300         0.420587     0.0  0.279710\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279710\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202500e-10  -0.268289     0.042973         0.269670     0.0  0.279710\n",
       "4        274   snp2832_m0_.46m1_.1  4.0   3274.0  3274.0  7.069762e-04   0.170421     0.050003         0.151124     0.0  0.542046\n",
       "5         13  snp1413_m0_.04m1_.03  3.0   2013.0  2013.0  8.161237e-04  -0.148719     0.044157         0.149377     0.0  0.279710\n",
       "6        214   snp2804_m0_.16m1_.3  3.0   2214.0  2214.0  1.239806e-03   0.150705     0.046396         0.144180     0.0  0.279710\n",
       "7        117   snp751_m0_.04m1_.25  1.0    117.0   117.0  1.527432e-03  -0.152430     0.047827         0.141523     0.0  0.614963\n",
       "8        265  snp1440_m0_.35m1_.32  4.0   3265.0  3265.0  1.771049e-03   0.136281     0.043358         0.139610     0.0  0.542046\n",
       "9        307  snp2162_m0_.61m1_.42  2.0   1307.0  1307.0  1.816577e-03  -0.143296     0.045700         0.139280     0.0  0.534262"
      ],
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251925e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202500e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>274</td>\n",
       "      <td>snp2832_m0_.46m1_.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>7.069762e-04</td>\n",
       "      <td>0.170421</td>\n",
       "      <td>0.050003</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>snp1413_m0_.04m1_.03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.161237e-04</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>snp2804_m0_.16m1_.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>1.239806e-03</td>\n",
       "      <td>0.150705</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.144180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>snp751_m0_.04m1_.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.527432e-03</td>\n",
       "      <td>-0.152430</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.141523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265</td>\n",
       "      <td>snp1440_m0_.35m1_.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>1.771049e-03</td>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>307</td>\n",
       "      <td>snp2162_m0_.61m1_.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1.816577e-03</td>\n",
       "      <td>-0.143296</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.139280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 2
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Notice that *single_snp_scale* here has the same inputs as *single_snp* and produces the same output (to many decimal places). On a small example like this, *single_snp_scale* is slower than *single_snp* (3 minutes vs. 3 seconds) because of the overhead of dividing the work for clustering. On examples that take several hours, their run time is comparable. And, of course, *single_snp_scale* can complete larger jobs that *single_snp* cannot."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Multiple Phenotypes\n",
    "\n",
    "Suppose we wish to analyze 10 phenotypes. We could do 10 separate runs, but that would cost 10 times more in computer resources. (Likewise, 1000 phenotypes would take 1000 times more.)\n",
    "\n",
    "Happily, single_snp_scale can process multiple phenotypes almost as fast as 1. The only extra requirement that is the phenotypes must be prepared so that they have no missing values.\n",
    "\n",
    "In the example below, we add 9 random phenotypes (with missing values) to our previous single phenotype. We then fill in the missing values with each phenotype's mean value. The run takes only a few seconds longer that the single phenotype run. The resulting Manhattan plot appears much the same with the best SNPs from the first phenotype again rising to the top.\n",
    "\n",
    "##### Note\n",
    "\n",
    "For some users, every phenotype value is so expensive to measure that diluting any missing values with a mean value is unacceptable. For those users, we recommend making one run per phenotype."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "%%time\r\n",
    "import numpy as np\r\n",
    "from pysnptools.snpreader import SnpData,Pheno\r\n",
    "\r\n",
    "\r\n",
    "################################################################\r\n",
    "# We could read the pheno data into memory like this\r\n",
    "#     pheno_data = Pheno(some_pheno_file_with_multiple_phenotypes).read()\r\n",
    "################################################################\r\n",
    "# but for this example, we'll instead add 9 random phenotypes to the previous phenotype data\r\n",
    "################################################################\r\n",
    "old_pheno = Pheno(pheno_fn)\r\n",
    "seed = 1\r\n",
    "np.random.seed(seed)\r\n",
    "pheno_count = 10\r\n",
    "pheno_data = SnpData(iid=old_pheno.iid,sid=['pheno{0}'.format(i) for i in range(pheno_count)],val=np.random.randn(old_pheno.iid_count,pheno_count)*3+2)\r\n",
    "for missing_index in range(20): #Add missing data to the random phenos\r\n",
    "     pheno_data.val[np.random.randint(pheno_data.iid_count),np.random.randint(pheno_data.sid_count)] = np.nan\r\n",
    "pheno_data.val[:,0] = old_pheno.read().val[:,0] #Stick the old phenotype into the new\r\n",
    "\r\n",
    "################################################################\r\n",
    "# When single_snp_scale is run on multiple phenotypes, there must not be any missing\r\n",
    "# phenotype data. Here we remove our data's few missing value:\r\n",
    "\r\n",
    "# Remove any individuals lacking even a single value in any phenotype\r\n",
    "pheno_wo_bad_indivs = pheno_data[(pheno_data.val==pheno_data.val).any(1),:]\r\n",
    "# Standardize the pheno data to mean 0 and std dev 1 and then fill any missing values with 0\r\n",
    "pheno = pheno_wo_bad_indivs.read().standardize()\r\n",
    "\r\n",
    "\r\n",
    "# Run GWAS\r\n",
    "results_df = single_snp_scale(bed_fn, pheno, covar=cov_fn, count_A1=False)\r\n",
    "\r\n",
    "# manhattan plot\r\n",
    "import pylab\r\n",
    "import fastlmm.util.util as flutil\r\n",
    "pylab.rcParams['figure.figsize'] = (10.0, 8.0)\r\n",
    "flutil.manhattan_plot(results_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5,xaxis_unit_bp=False)\r\n",
    "pylab.show()\r\n",
    "\r\n",
    "# qq plot\r\n",
    "from fastlmm.util.stats import plotp\r\n",
    "plotp.qqplot(results_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\r\n",
    "\r\n",
    "# print head of results data frame\r\n",
    "import pandas as pd\r\n",
    "pd.set_option('display.width', 1000)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lambda=0.9519\n",
      "Wall time: 2min 37s\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2   Pheno\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451116  pheno0\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251919e-23  -0.416495     0.040300         0.420587     0.0  0.279711  pheno0\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279711  pheno0\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202498e-10  -0.268289     0.042973         0.269670     0.0  0.279711  pheno0\n",
       "4        384   snp879_m0_.56m1_.22  1.0    384.0   384.0  4.247890e-05   0.182206     0.044113         0.182174     0.0  0.000000  pheno9\n",
       "5        408  snp2540_m0_.11m1_.09  2.0   1408.0  1408.0  5.363469e-05  -0.178662     0.043847         0.179795     0.0  0.000000  pheno3\n",
       "6        811  snp3024_m0_.32m1_.46  1.0    811.0   811.0  7.623969e-05  -0.175790     0.044065         0.176147     0.0  0.000000  pheno5\n",
       "7        185    snp71_m0_.38m1_.47  5.0   4185.0  4185.0  7.937905e-05  -0.175679     0.044147         0.175723     0.0  0.000000  pheno1\n",
       "8        413  snp3661_m0_.21m1_.21  2.0   1413.0  1413.0  1.054421e-04   0.172082     0.044020         0.172717     0.0  0.000000  pheno3\n",
       "9        835  snp4723_m0_.04m1_.05  4.0   3835.0  3835.0  1.136454e-04   0.171602     0.044108         0.171916     0.0  0.000000  pheno4"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "      <th>Pheno</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451116</td>\n",
       "      <td>pheno0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251919e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279711</td>\n",
       "      <td>pheno0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279711</td>\n",
       "      <td>pheno0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202498e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279711</td>\n",
       "      <td>pheno0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>384</td>\n",
       "      <td>snp879_m0_.56m1_.22</td>\n",
       "      <td>1.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>4.247890e-05</td>\n",
       "      <td>0.182206</td>\n",
       "      <td>0.044113</td>\n",
       "      <td>0.182174</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>408</td>\n",
       "      <td>snp2540_m0_.11m1_.09</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1408.0</td>\n",
       "      <td>1408.0</td>\n",
       "      <td>5.363469e-05</td>\n",
       "      <td>-0.178662</td>\n",
       "      <td>0.043847</td>\n",
       "      <td>0.179795</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>811</td>\n",
       "      <td>snp3024_m0_.32m1_.46</td>\n",
       "      <td>1.0</td>\n",
       "      <td>811.0</td>\n",
       "      <td>811.0</td>\n",
       "      <td>7.623969e-05</td>\n",
       "      <td>-0.175790</td>\n",
       "      <td>0.044065</td>\n",
       "      <td>0.176147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>185</td>\n",
       "      <td>snp71_m0_.38m1_.47</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4185.0</td>\n",
       "      <td>4185.0</td>\n",
       "      <td>7.937905e-05</td>\n",
       "      <td>-0.175679</td>\n",
       "      <td>0.044147</td>\n",
       "      <td>0.175723</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>413</td>\n",
       "      <td>snp3661_m0_.21m1_.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>1413.0</td>\n",
       "      <td>1.054421e-04</td>\n",
       "      <td>0.172082</td>\n",
       "      <td>0.044020</td>\n",
       "      <td>0.172717</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>835</td>\n",
       "      <td>snp4723_m0_.04m1_.05</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3835.0</td>\n",
       "      <td>3835.0</td>\n",
       "      <td>1.136454e-04</td>\n",
       "      <td>0.171602</td>\n",
       "      <td>0.044108</td>\n",
       "      <td>0.171916</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>pheno4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 3
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Caching Results\n",
    "\n",
    "The *single_snp_scale* function can cache its work. If it is interrupted, it can be restarted just by re-running. It will pick up where it left off.\n",
    "\n",
    "> *Aside*: If no cache is explicitly specified, *single_snp_scale* will use an automatically erasing, temporary directory.\n",
    "> If the TEMP environment variable is set, Python places this temporary directory under it. On the other hand, if a cache is\n",
    "> specified, *single_snp_scale* will *not* automatically erase it; instead, you'll have to decide when to erase it."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Set to a folder on your machine, ideally an SSD drive. (Best is a fast M.2. SSD.)\r\n",
    "cache_top = r'c:\\deldir'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# The first run again, now with an explicit cache, takes about 1.5 minutes.\r\n",
    "# assumes that folder \"cache_top+'/small'\" doesn't yet exist\r\n",
    "results_df = single_snp_scale(bed_fn, pheno_fn, covar=cov_fn, count_A1=False, cache=cache_top+'/small')\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451117\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251925e-23  -0.416495     0.040300         0.420587     0.0  0.279710\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279710\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202500e-10  -0.268289     0.042973         0.269670     0.0  0.279710\n",
       "4        274   snp2832_m0_.46m1_.1  4.0   3274.0  3274.0  7.069762e-04   0.170421     0.050003         0.151124     0.0  0.542046\n",
       "5         13  snp1413_m0_.04m1_.03  3.0   2013.0  2013.0  8.161237e-04  -0.148719     0.044157         0.149377     0.0  0.279710\n",
       "6        214   snp2804_m0_.16m1_.3  3.0   2214.0  2214.0  1.239806e-03   0.150705     0.046396         0.144180     0.0  0.279710\n",
       "7        117   snp751_m0_.04m1_.25  1.0    117.0   117.0  1.527432e-03  -0.152430     0.047827         0.141523     0.0  0.614963\n",
       "8        265  snp1440_m0_.35m1_.32  4.0   3265.0  3265.0  1.771049e-03   0.136281     0.043358         0.139610     0.0  0.542046\n",
       "9        307  snp2162_m0_.61m1_.42  2.0   1307.0  1307.0  1.816577e-03  -0.143296     0.045700         0.139280     0.0  0.534262"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251925e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202500e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>274</td>\n",
       "      <td>snp2832_m0_.46m1_.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>7.069762e-04</td>\n",
       "      <td>0.170421</td>\n",
       "      <td>0.050003</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>snp1413_m0_.04m1_.03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.161237e-04</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>snp2804_m0_.16m1_.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>1.239806e-03</td>\n",
       "      <td>0.150705</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.144180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>snp751_m0_.04m1_.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.527432e-03</td>\n",
       "      <td>-0.152430</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.141523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265</td>\n",
       "      <td>snp1440_m0_.35m1_.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>1.771049e-03</td>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>307</td>\n",
       "      <td>snp2162_m0_.61m1_.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1.816577e-03</td>\n",
       "      <td>-0.143296</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.139280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# A second run uses the cache and returns instantly.\r\n",
    "results_df = single_snp_scale(bed_fn, pheno_fn, covar=cov_fn, count_A1=False, cache=cache_top+'/small')\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451117\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251925e-23  -0.416495     0.040300         0.420587     0.0  0.279710\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279710\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202500e-10  -0.268289     0.042973         0.269670     0.0  0.279710\n",
       "4        274   snp2832_m0_.46m1_.1  4.0   3274.0  3274.0  7.069762e-04   0.170421     0.050003         0.151124     0.0  0.542046\n",
       "5         13  snp1413_m0_.04m1_.03  3.0   2013.0  2013.0  8.161237e-04  -0.148719     0.044157         0.149377     0.0  0.279710\n",
       "6        214   snp2804_m0_.16m1_.3  3.0   2214.0  2214.0  1.239806e-03   0.150705     0.046396         0.144180     0.0  0.279710\n",
       "7        117   snp751_m0_.04m1_.25  1.0    117.0   117.0  1.527432e-03  -0.152430     0.047827         0.141523     0.0  0.614963\n",
       "8        265  snp1440_m0_.35m1_.32  4.0   3265.0  3265.0  1.771049e-03   0.136281     0.043358         0.139610     0.0  0.542046\n",
       "9        307  snp2162_m0_.61m1_.42  2.0   1307.0  1307.0  1.816577e-03  -0.143296     0.045700         0.139280     0.0  0.534262"
      ],
      "text/html": [
       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251925e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202500e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>274</td>\n",
       "      <td>snp2832_m0_.46m1_.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>7.069762e-04</td>\n",
       "      <td>0.170421</td>\n",
       "      <td>0.050003</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>snp1413_m0_.04m1_.03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.161237e-04</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>snp2804_m0_.16m1_.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>1.239806e-03</td>\n",
       "      <td>0.150705</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.144180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>snp751_m0_.04m1_.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.527432e-03</td>\n",
       "      <td>-0.152430</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.141523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265</td>\n",
       "      <td>snp1440_m0_.35m1_.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>1.771049e-03</td>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>307</td>\n",
       "      <td>snp2162_m0_.61m1_.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1.816577e-03</td>\n",
       "      <td>-0.143296</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.139280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Running on Multiple Processors (or a Cluster)\n",
    "\n",
    "To run on a cluster, we specify must 'runner', a module object that tells how to submit and run batches of work. Currently, the only provided runners are 'Local' (the default) and 'LocalMultiProc' (run as multiple Python processes). In the past we've implemented runners for Hadoop, Windows HPC, and Azure Batch. New runners can be created for almost any batch-like cluster.\n",
    "\n",
    "Here is an example of running the small job on five additional Python processes:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "from pysnptools.util.mapreduce1.runner import LocalMultiProc\r\n",
    "# assumes that folder \"cache_top+'/small5proc'\" doesn't yet exist\r\n",
    "\r\n",
    "runner = LocalMultiProc(taskcount=5) #Run on 5 additional Python processes\r\n",
    "\r\n",
    "results_df = single_snp_scale(bed_fn, pheno_fn, covar=cov_fn, count_A1=False, cache=cache_top+'/small5proc', runner=runner)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451117\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251925e-23  -0.416495     0.040300         0.420587     0.0  0.279710\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279710\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202500e-10  -0.268289     0.042973         0.269670     0.0  0.279710\n",
       "4        274   snp2832_m0_.46m1_.1  4.0   3274.0  3274.0  7.069762e-04   0.170421     0.050003         0.151124     0.0  0.542046\n",
       "5         13  snp1413_m0_.04m1_.03  3.0   2013.0  2013.0  8.161237e-04  -0.148719     0.044157         0.149377     0.0  0.279710\n",
       "6        214   snp2804_m0_.16m1_.3  3.0   2214.0  2214.0  1.239806e-03   0.150705     0.046396         0.144180     0.0  0.279710\n",
       "7        117   snp751_m0_.04m1_.25  1.0    117.0   117.0  1.527432e-03  -0.152430     0.047827         0.141523     0.0  0.614963\n",
       "8        265  snp1440_m0_.35m1_.32  4.0   3265.0  3265.0  1.771049e-03   0.136281     0.043358         0.139610     0.0  0.542046\n",
       "9        307  snp2162_m0_.61m1_.42  2.0   1307.0  1307.0  1.816576e-03  -0.143296     0.045700         0.139280     0.0  0.534262"
      ],
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251925e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202500e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>274</td>\n",
       "      <td>snp2832_m0_.46m1_.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>7.069762e-04</td>\n",
       "      <td>0.170421</td>\n",
       "      <td>0.050003</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>snp1413_m0_.04m1_.03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.161237e-04</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>snp2804_m0_.16m1_.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>1.239806e-03</td>\n",
       "      <td>0.150705</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.144180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>snp751_m0_.04m1_.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.527432e-03</td>\n",
       "      <td>-0.152430</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.141523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265</td>\n",
       "      <td>snp1440_m0_.35m1_.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>1.771049e-03</td>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>307</td>\n",
       "      <td>snp2162_m0_.61m1_.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1.816576e-03</td>\n",
       "      <td>-0.143296</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.139280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "For more on using runners, see the [PySnpTools MapReduce1 API documentation](http://fastlmm.github.io/PySnpTools/#module-pysnptools.util.mapreduce1). \n",
    "For information on implementing for a new runner, see the [corresponding source code](https://github.com/fastlmm.github.io/PySnpTools/tree/master/pysnptools/util).\n",
    "The [single_snp_scale API Documentation](http://fastlmm.github.io/FaST-LMM/#single-snp-scale) also includes information on how to specify different runners for different phases of the calculations."
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Distributing Files\n",
    "\n",
    "Consider a run for 1 million individuals and 1 million SNPs on a cluster with 100 nodes. As part of the run, every node in the cluster will  access every byte in a 400GB file about 10,000 times. The nodes also must access pieces of a 2.5TB file.\n",
    "\n",
    "The naive way to access files on Linux is to just mount them via [FUSE](https://en.wikipedia.org/wiki/Filesystem_in_Userspace). But as Linus Torvalds warns:\n",
    "\n",
    "> \"Userspace filesystem \\[e.g. FUSE\\]? The problem is right there. Always has been.\n",
    "> People who think that userspace filesystems are realistic for anything but toys are just misguided.\"\n",
    "[https://lkml.org/lkml/2011/6/9/462](https://lkml.org/lkml/2011/6/9/462)\n",
    "\n",
    "So perhaps we should use a different method. We can make everything 10,000 times faster by downloading the 400GB file once to an SSD drive on each node. How should we do these 100 copies? Again, using FUSE would be simplest way, but many clusters offer file-copy methods orders of magnitude faster.\n",
    "\n",
    "In any case, the *single_snp_scale* function can work with *any* method of distributing files. It just requires a [file_cache module](http://fastlmm.github.io/PySnpTools/#module-pysnptools.util.filecache) describing the method of choice.\n",
    "\n",
    "> *Aside*: For the run described in the [paper](https://www.biorxiv.org/content/10.1101/154682v3), \n",
    "> we used 'AzureP2P', a *file_cache* method that stored one copy of every file in\n",
    "> Azure blog storage. After copying once from blog storage, the module then distributes additional copies via a peer-to-peer\n",
    "> tree copy over the cluster's fast local network.\n",
    "\n",
    "If no  distribution method is given to *single_snp_scale*, it assumes all files are local. To specify a different distribution method give it:\n",
    "* A *file_cache* cache directory\n",
    "* SNPs data accessed via a *file_cache*\n",
    "\n",
    "Here is an example. We create a *PeerToPeer* file_cache that gives every process on the machine a private bit of local storage. Then we upload the SNP data into that file_cache run our small sample problem."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "#Change the line 'return ip_pid, r'c:/deldir/peertopeer1/{0}'.format(ip_pid)'\r\n",
    "# to point to your SSD drive\r\n",
    "# See the \"if desired\" comment below on how to clear the file_cache\r\n",
    "\r\n",
    "from pysnptools.util.filecache import PeerToPeer\r\n",
    "from pysnptools.snpreader import Bed, DistributedBed\r\n",
    "from pysnptools.util.mapreduce1.runner import LocalMultiProc\r\n",
    "from fastlmm.association import single_snp_scale\r\n",
    "from fastlmm.util import example_file # Download and return local file name\r\n",
    "\r\n",
    "bed_fn = example_file('tests/datasets/synth/all.*','*.bed')\r\n",
    "pheno_fn = example_file(\"tests/datasets/synth/pheno_10_causals.txt\")\r\n",
    "cov_fn = example_file(\"tests/datasets/synth/cov.txt\")\r\n",
    "\r\n",
    "#Every Python process will get private local storage based its process id (and this computer's ipaddress)\r\n",
    "#A directory in \"common\" tracks the locations of each file. Copying is peer-to-peer.\r\n",
    "def id_and_path_function():\r\n",
    "     from pysnptools.util.filecache import ip_address_pid\r\n",
    "     ip_pid = ip_address_pid()\r\n",
    "     #Need to put the 'cache_top' here explicitly.\r\n",
    "     return ip_pid, r'c:/deldir/peertopeer1/{0}'.format(ip_pid)\r\n",
    "file_cache = PeerToPeer(common_directory=cache_top+'/peertopeer1/common',id_and_path_function=id_and_path_function)\r\n",
    "\r\n",
    "# If desired, clear the file_cache\r\n",
    "#file_cache.rmtree()\r\n",
    "\r\n",
    "# The test SNPs (and G0, if given) must be accessible from every node, so we upload them into\r\n",
    "# a DistributedBed. (There will be 2 Bed files per chrom.)\r\n",
    "test_snps_cache = file_cache.join('test_snps')\r\n",
    "if not next(test_snps_cache.walk(),None): #If no files in the test_snps folder, upload data (takes a few seconds)\r\n",
    "    test_snps = DistributedBed.write(test_snps_cache, Bed(bed_fn,count_A1=False), piece_per_chrom_count=2)\r\n",
    "else:\r\n",
    "    test_snps = DistributedBed(test_snps_cache)\r\n",
    "\r\n",
    "runner = LocalMultiProc(taskcount=5) #Run on 5 additional Python processes\r\n",
    "\r\n",
    "# We set cache=file_cache so that intermetiate files will be accessible from every node.\r\n",
    "# We use give the DistributedBed as the source of SNP data.\r\n",
    "results_df = single_snp_scale(test_snps, pheno_fn, covar=cov_fn, count_A1=False, cache=file_cache, runner=runner)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index                   SNP  Chr  GenDist  ChrPos        PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0         52   snp495_m0_.01m1_.04  5.0   4052.0  4052.0  2.990684e-23   0.418653     0.040052         0.424521     0.0  0.451117\n",
       "1        392   snp1422_m0_.49m1_.5  3.0   2392.0  2392.0  8.251922e-23  -0.416495     0.040300         0.420587     0.0  0.279710\n",
       "2        650  snp1200_m0_.37m1_.36  3.0   2650.0  2650.0  3.048007e-14   0.328870     0.042021         0.331240     0.0  0.279710\n",
       "3          3   snp433_m0_.14m1_.11  3.0   2003.0  2003.0  9.202499e-10  -0.268289     0.042973         0.269670     0.0  0.279710\n",
       "4        274   snp2832_m0_.46m1_.1  4.0   3274.0  3274.0  7.069762e-04   0.170421     0.050003         0.151124     0.0  0.542046\n",
       "5         13  snp1413_m0_.04m1_.03  3.0   2013.0  2013.0  8.161238e-04  -0.148719     0.044157         0.149377     0.0  0.279710\n",
       "6        214   snp2804_m0_.16m1_.3  3.0   2214.0  2214.0  1.239806e-03   0.150705     0.046396         0.144180     0.0  0.279710\n",
       "7        117   snp751_m0_.04m1_.25  1.0    117.0   117.0  1.527432e-03  -0.152430     0.047827         0.141523     0.0  0.614963\n",
       "8        265  snp1440_m0_.35m1_.32  4.0   3265.0  3265.0  1.771049e-03   0.136281     0.043358         0.139610     0.0  0.542046\n",
       "9        307  snp2162_m0_.61m1_.42  2.0   1307.0  1307.0  1.816577e-03  -0.143296     0.045700         0.139280     0.0  0.534262"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52</td>\n",
       "      <td>snp495_m0_.01m1_.04</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>4052.0</td>\n",
       "      <td>2.990684e-23</td>\n",
       "      <td>0.418653</td>\n",
       "      <td>0.040052</td>\n",
       "      <td>0.424521</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.451117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>392</td>\n",
       "      <td>snp1422_m0_.49m1_.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>2392.0</td>\n",
       "      <td>8.251922e-23</td>\n",
       "      <td>-0.416495</td>\n",
       "      <td>0.040300</td>\n",
       "      <td>0.420587</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>650</td>\n",
       "      <td>snp1200_m0_.37m1_.36</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>2650.0</td>\n",
       "      <td>3.048007e-14</td>\n",
       "      <td>0.328870</td>\n",
       "      <td>0.042021</td>\n",
       "      <td>0.331240</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>snp433_m0_.14m1_.11</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>9.202499e-10</td>\n",
       "      <td>-0.268289</td>\n",
       "      <td>0.042973</td>\n",
       "      <td>0.269670</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>274</td>\n",
       "      <td>snp2832_m0_.46m1_.1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>3274.0</td>\n",
       "      <td>7.069762e-04</td>\n",
       "      <td>0.170421</td>\n",
       "      <td>0.050003</td>\n",
       "      <td>0.151124</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13</td>\n",
       "      <td>snp1413_m0_.04m1_.03</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>8.161238e-04</td>\n",
       "      <td>-0.148719</td>\n",
       "      <td>0.044157</td>\n",
       "      <td>0.149377</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>214</td>\n",
       "      <td>snp2804_m0_.16m1_.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>2214.0</td>\n",
       "      <td>1.239806e-03</td>\n",
       "      <td>0.150705</td>\n",
       "      <td>0.046396</td>\n",
       "      <td>0.144180</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.279710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>117</td>\n",
       "      <td>snp751_m0_.04m1_.25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>1.527432e-03</td>\n",
       "      <td>-0.152430</td>\n",
       "      <td>0.047827</td>\n",
       "      <td>0.141523</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.614963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>265</td>\n",
       "      <td>snp1440_m0_.35m1_.32</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>3265.0</td>\n",
       "      <td>1.771049e-03</td>\n",
       "      <td>0.136281</td>\n",
       "      <td>0.043358</td>\n",
       "      <td>0.139610</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.542046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>307</td>\n",
       "      <td>snp2162_m0_.61m1_.42</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1307.0</td>\n",
       "      <td>1.816577e-03</td>\n",
       "      <td>-0.143296</td>\n",
       "      <td>0.045700</td>\n",
       "      <td>0.139280</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.534262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Larger Example\n",
    "\n",
    "Let's end this notebook with an example that is too big to run on *single_snp*, but that runs (in about 16 hours) on one machine via *single_snp_scale*. It requires an SSD drive with 1/2 TB free."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Set to a folder on your machine, ideally an SSD drive. My 'm:' drive is a fast M.2. SSD.\r\n",
    "cache_top = r'm:\\deldir'"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Generate random SNPs, pheno, and covar data, if needed. (Can take 6 hours)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "from pysnptools.util.filecache import LocalCache\r\n",
    "file_cache_top = LocalCache(cache_top)\r\n",
    "\r\n",
    "seed = 1\r\n",
    "iid_count = 250*1000 # number of individuals\r\n",
    "sid_count = 10*1000 # number of SNPs\r\n",
    "chrom_count = 10\r\n",
    "piece_per_chrom_count = 25 #Number of pieces for each chromosome, e.g. 100\r\n",
    "test_snps_cache = file_cache_top.join('testsnps_{0}_{1}_{2}_{3}'.format(seed,chrom_count,iid_count,sid_count))\r\n",
    "\r\n",
    "if not next(test_snps_cache.walk(),None): #If no files in the test_snps folder, generate data (takes about 6 hours)\r\n",
    "    from pysnptools.snpreader import SnpGen\r\n",
    "    from pysnptools.util.mapreduce1.runner import LocalMultiProc\r\n",
    "    \r\n",
    "    snpgen = SnpGen(seed=seed,iid_count=iid_count,sid_count=sid_count,chrom_count=chrom_count,block_size=1000) #Create an on-the-fly SNP generator\r\n",
    "    snp_gen_runner = LocalMultiProc(5)\r\n",
    "    #Write random SNP data to a DistributedBed\r\n",
    "    test_snps = DistributedBed.write(test_snps_cache,snpgen,piece_per_chrom_count=piece_per_chrom_count,runner=snp_gen_runner)\r\n",
    "else:\r\n",
    "    test_snps = DistributedBed(test_snps_cache)\r\n",
    "\r\n",
    "#Generate random pheno and covar\r\n",
    "import numpy as np\r\n",
    "from pysnptools.snpreader import SnpData\r\n",
    "np.random.seed(seed)\r\n",
    "pheno = SnpData(iid=test_snps.iid,sid=['pheno'],val=np.random.randn(test_snps.iid_count,1)*3+2)\r\n",
    "covar = SnpData(iid=test_snps.iid,sid=['covar1','covar2'],val=np.random.randn(test_snps.iid_count,2)*2-3)\r\n",
    "\r\n",
    "test_snps.shape"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(250000, 10000)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Do the run. (Takes about 8.5 hours)"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "from fastlmm.association import single_snp_scale\r\n",
    "from pysnptools.util.mapreduce1.runner import LocalMultiProc\r\n",
    "\r\n",
    "sss_cache = file_cache_top.join('bigger')\r\n",
    "sss_runner = LocalMultiProc(5)\r\n",
    "\r\n",
    "results_df = single_snp_scale(test_snps, pheno, G0=test_snps, covar=covar, cache=sss_cache, runner=sss_runner)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index       SNP   Chr  GenDist        ChrPos    PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0        556  sid_5408   5.0      0.0  1.042141e+09  0.000050   0.027517     0.006787         0.008108     0.0  0.000000\n",
       "1        706  sid_4504   4.0      0.0  8.680462e+08  0.000059  -0.027252     0.006782         0.008037     0.0  0.000000\n",
       "2        543  sid_3231   3.0      0.0  6.226361e+08  0.000065   0.027075     0.006779         0.007988     0.0  0.000174\n",
       "3        576  sid_6434   6.0      0.0  1.239995e+09  0.000279   0.024655     0.006785         0.007268     0.0  0.000000\n",
       "4        474  sid_4272   4.0      0.0  8.233398e+08  0.000280   0.024639     0.006782         0.007266     0.0  0.000000\n",
       "5        250  sid_6108   6.0      0.0  1.177175e+09  0.000488   0.023634     0.006778         0.006974     0.0  0.000000\n",
       "6        770  sid_8465   8.0      0.0  1.631379e+09  0.000587  -0.023333     0.006788         0.006875     0.0  0.000000\n",
       "7        902  sid_4700   4.0      0.0  9.058154e+08  0.000788  -0.022751     0.006777         0.006714     0.0  0.000000\n",
       "8        795  sid_3483   3.0      0.0  6.711965e+08  0.000801   0.022723     0.006778         0.006705     0.0  0.000174\n",
       "9          6  sid_9258  10.0      0.0  1.784156e+09  0.000816  -0.022690     0.006779         0.006694     0.0  0.000000"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>556</td>\n",
       "      <td>sid_5408</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.042141e+09</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.027517</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>0.008108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>706</td>\n",
       "      <td>sid_4504</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.680462e+08</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.027252</td>\n",
       "      <td>0.006782</td>\n",
       "      <td>0.008037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>543</td>\n",
       "      <td>sid_3231</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.226361e+08</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.027075</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>576</td>\n",
       "      <td>sid_6434</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.239995e+09</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.024655</td>\n",
       "      <td>0.006785</td>\n",
       "      <td>0.007268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>474</td>\n",
       "      <td>sid_4272</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.233398e+08</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.024639</td>\n",
       "      <td>0.006782</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>250</td>\n",
       "      <td>sid_6108</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.177175e+09</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.023634</td>\n",
       "      <td>0.006778</td>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>770</td>\n",
       "      <td>sid_8465</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.631379e+09</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>0.006788</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>902</td>\n",
       "      <td>sid_4700</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.058154e+08</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>-0.022751</td>\n",
       "      <td>0.006777</td>\n",
       "      <td>0.006714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>795</td>\n",
       "      <td>sid_3483</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.711965e+08</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.006778</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>sid_9258</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.784156e+09</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>-0.022690</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.006694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# manhattan plot\r\n",
    "import pylab\r\n",
    "import fastlmm.util.util as flutil\r\n",
    "flutil.manhattan_plot(results_df[[\"Chr\", \"ChrPos\", \"PValue\"]].values,pvalue_line=1e-5,xaxis_unit_bp=False)\r\n",
    "pylab.show()\r\n",
    "\r\n",
    "# qq plot\r\n",
    "from fastlmm.util.stats import plotp\r\n",
    "plotp.qqplot(results_df[\"PValue\"].values, xlim=[0,5], ylim=[0,5])\r\n",
    "\r\n",
    "# print head of results data frame\r\n",
    "import pandas as pd\r\n",
    "pd.set_option('display.width', 1000)\r\n",
    "results_df.head(n=10)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ],
      "image/png": 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"
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     "metadata": {
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    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "lambda=0.9866\n"
     ]
    },
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\Carl\\Anaconda2\\envs\\py3\\lib\\site-packages\\numpy\\lib\\scimath.py:122: RuntimeWarning: invalid value encountered in less\n",
      "  if any(isreal(x) & (x < 0)):\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   sid_index       SNP   Chr  GenDist        ChrPos    PValue  SnpWeight  SnpWeightSE  SnpFractVarExpl  Mixing    Nullh2\n",
       "0        556  sid_5408   5.0      0.0  1.042141e+09  0.000050   0.027517     0.006787         0.008108     0.0  0.000000\n",
       "1        706  sid_4504   4.0      0.0  8.680462e+08  0.000059  -0.027252     0.006782         0.008037     0.0  0.000000\n",
       "2        543  sid_3231   3.0      0.0  6.226361e+08  0.000065   0.027075     0.006779         0.007988     0.0  0.000174\n",
       "3        576  sid_6434   6.0      0.0  1.239995e+09  0.000279   0.024655     0.006785         0.007268     0.0  0.000000\n",
       "4        474  sid_4272   4.0      0.0  8.233398e+08  0.000280   0.024639     0.006782         0.007266     0.0  0.000000\n",
       "5        250  sid_6108   6.0      0.0  1.177175e+09  0.000488   0.023634     0.006778         0.006974     0.0  0.000000\n",
       "6        770  sid_8465   8.0      0.0  1.631379e+09  0.000587  -0.023333     0.006788         0.006875     0.0  0.000000\n",
       "7        902  sid_4700   4.0      0.0  9.058154e+08  0.000788  -0.022751     0.006777         0.006714     0.0  0.000000\n",
       "8        795  sid_3483   3.0      0.0  6.711965e+08  0.000801   0.022723     0.006778         0.006705     0.0  0.000174\n",
       "9          6  sid_9258  10.0      0.0  1.784156e+09  0.000816  -0.022690     0.006779         0.006694     0.0  0.000000"
      ],
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sid_index</th>\n",
       "      <th>SNP</th>\n",
       "      <th>Chr</th>\n",
       "      <th>GenDist</th>\n",
       "      <th>ChrPos</th>\n",
       "      <th>PValue</th>\n",
       "      <th>SnpWeight</th>\n",
       "      <th>SnpWeightSE</th>\n",
       "      <th>SnpFractVarExpl</th>\n",
       "      <th>Mixing</th>\n",
       "      <th>Nullh2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>556</td>\n",
       "      <td>sid_5408</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.042141e+09</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>0.027517</td>\n",
       "      <td>0.006787</td>\n",
       "      <td>0.008108</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>706</td>\n",
       "      <td>sid_4504</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.680462e+08</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>-0.027252</td>\n",
       "      <td>0.006782</td>\n",
       "      <td>0.008037</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>543</td>\n",
       "      <td>sid_3231</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.226361e+08</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>0.027075</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.007988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>576</td>\n",
       "      <td>sid_6434</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.239995e+09</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.024655</td>\n",
       "      <td>0.006785</td>\n",
       "      <td>0.007268</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>474</td>\n",
       "      <td>sid_4272</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.233398e+08</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.024639</td>\n",
       "      <td>0.006782</td>\n",
       "      <td>0.007266</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>250</td>\n",
       "      <td>sid_6108</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.177175e+09</td>\n",
       "      <td>0.000488</td>\n",
       "      <td>0.023634</td>\n",
       "      <td>0.006778</td>\n",
       "      <td>0.006974</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>770</td>\n",
       "      <td>sid_8465</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.631379e+09</td>\n",
       "      <td>0.000587</td>\n",
       "      <td>-0.023333</td>\n",
       "      <td>0.006788</td>\n",
       "      <td>0.006875</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>902</td>\n",
       "      <td>sid_4700</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.058154e+08</td>\n",
       "      <td>0.000788</td>\n",
       "      <td>-0.022751</td>\n",
       "      <td>0.006777</td>\n",
       "      <td>0.006714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>795</td>\n",
       "      <td>sid_3483</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.711965e+08</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.022723</td>\n",
       "      <td>0.006778</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>sid_9258</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.784156e+09</td>\n",
       "      <td>0.000816</td>\n",
       "      <td>-0.022690</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.006694</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ],
      "image/png": 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uemVDP3797F1+/eoDrr32Wp599tka3Qbzwlry8vLw8/MjOTmZhIQEOdy9TJcMQK31EqXUt0BP66ZxWuvjji1LCNeSmZlJRkaGTeGntWbpO2+S+tWH3HjjjYwePdpu4VdcXExRURGJiYm0a9fOIRdSvMklA1BZ/msZADTTWk9TSjVWSvXQWqc5vjwhnM9gMLB3717Cw8Mv2dPSWvPaa6+xfPlybr31Vp566im7zLYov8gREhJCnz59amWxBG9gyyHwa4AZuBqYBuQDnwDdHViXEC6hqKiIgwcPEhQUVOX5u3yjZvXhEn7/4nW++eoL7rzzTh5//PEaH5qWr9hSWloqFzkcwJafZE+tdVel1G8AWuscpZR9r+EL4YJMJtPZix5BQUFVvnb14RJe/8fLFGz9nrvvvpu//e1vNQ6/srIy8vPziY6OpkuXLg5fKMEb2RKApUopX6yDopVS9bD0CIXwWFprtm7dSnZ29iWv+JpMJjZ+aAm/Iffcx98eeqBG4Xfu0JaOHTvSrFkzr1+0wFFsCcB/AJ8BsUqpmcAgYKJDqxLCyQ4ePMjBgweJjo6mtLS00teVlZUxe/ZsVv70E3/9618ZPnx4jT63vNcnQ1tqhy1Xgd9XSm0ErgEUcLvWeqfDKxPCSU6fPs3WrVsveTvL0tJSZsyYwerVqxkxYgTDhg277M8sn7+rtSYpKYkmTZpIr68W2HIVeCGwTGu9qBbqEcKpDAYD69evJzg4uMqLDUajkalTp7J27Vr+/ve/M3jw4Mv+zHN7fV27diU0NPSy9yWqx5ZD4E3ARKVUKyyHwsu01umXeI8Qbqe0tJS0tDS01lWOryspKWHy5MmkpaUx4L7HueG2Oy/r88rP9ZnNZjp37kxiYqL0+mrZJX/aWut3tdYDgR7AHuAFpdReh1cmRC3SWrN582by8vIuWkEl36j55qCRfKPmVF4hI54ex4YNG7j+/pHsjb+BX45Wfo6wMiaTiTNnzhAeHk7//v3lQoeTVGdAUQugDZAI/O6QaoRwkt27d1e6vNXaP8x8st+MsaiQTxZO5sjeHdz4wGgeHTLw7IIH1VE+rq9Dhw40b95cgs+JbDkH+AJwJ7Afy5JY07XWZ2x4XxCwGgi0fs5yrfWUmpUrhP0dPXqU33///aJ7epSv6NKprqLMaOR/iyZxbN9Obn54LCPuuq7C21xWxWw2k5eXR3h4OL1795ZxfS6gygC0ToMrAHpprU9Xc98lwNVa6wKllD/wq1LqW631ususVQi7O3PmDOnp6RXe06N8RZeb6hv46Y0p7N27l8mTJ9OvX79qf075HN5WrVrRunVrmc3hIqr8LWittVLqdq319OruWGutsYQngL/1q9IVpoWobUVFRaxbt46AgIAK1+jrE+9PYX4u3ywcz5HDh5g6dSq9e/eu1meUr9wSGBgoc3hdkC3/Da1TSnXXWm+o7s6tM0g2Yjl/uEhrvb66+xDCEcrX9istLa10sHGZ4QzfLhzHkcOHmTFjBj169KjWZ5SWlpKfn0/jxo3p1KmT3VeBFjVnSwD2Bx5RSmUABiyDobXWutOl3qi1NgFJSqko4DOlVAet9fZzX6OUGgGMAIiPjycjI+O8fWRlZdlQovvxxHa5S5u01hw6dIicnByCg4MxGo0XvSYrK4tx48Zx4sQJpkyZQlJSUoWvq2z/JSUlADRt2pSoqCiOHTtm1zbYg7v8vqqjum2yJQBvvLxS/qS1PqOUWgXcAGy/4LnFwGKA5ORknZiYeNH7K9rmCTyxXe7Qpt27d5OVlUV0dHSFMz1OnTrF2LFjOX36NHPmzKFdu3Y2997MZjO5ublER0eTnJzs8jclcoffV3VVp022jAPMBBphuaCRCRTa8j6lVD1rzw+lVDCWNQV32VyZEA5QfsW3shsaHT9+nJEjR5Kdnc2LL75IUlKSzfsuLi4mNzeXNm3a0KdPH5cPP2HbMJgpQDLQGliC5WLGe1juFleVBsC71vOAPsBHWuv/1qxcIS5fdnY26enphIWFVbhC89GjRxk9ejQGg4G5c+fStm1bm/artSY/Px9/f3+50OFmbDkEvgPogmVKHFrrY0qpSy5RobXean2fEE5nMBhYt24dgYGBFV7xPXz4MKNGjcJoNDJ9zlwOBjUlwagJD6h6WSuTyUReXh7169enS5cul1w3ULgWWwLQaB0OU74eoMzUFm6ltLSU9evXV3o3t4yMDEaPHo3JZOaO0S9wMCCRL3ZbprdVNdC5qKiIkpISOnbsSPPmzeXGRG7Iljk4Hyml3gCilFIPAz8Abzq2LCHsw2w2k56eTn5+foXn5Pbv38/IkU9TYoKbn36BVYWNAMWQ1v6VTnHTWpObm4uPjw/9+vWjRYsWEn5uypb1AOcqpa4F8rCcB5ystV7h8MqEqKHyW1keP368wru57dmzh2eeeQazbwCRg2ZSp2EiQ/wsA6ArO/QtP+SNj48nKSlJxva5OVsugoQCP2mtVyilWgOtlVL+WuvqL4EhRC3av38/+/fvv2iOL8DOnTsZO3YsoaGhTJszl306tsrggz8PeTt16kSzZs2k1+cBbDkHuBroo5SKxnL4mw7cDdzryMKEqIljx46xbds2IiMjL5rju23bNsaNG0dkZCTz58+nfv36tKpiX+XT2QICAujbty8xMTGOLV7UGlvOASqtdSGWFWFe0VrfAbRzbFlCXL6cnBw2bNhQ4XCXzZs3M3bsWGJiYli4cCH169evcl8mk4nCwkLq1atH//79Jfw8jE0BqJTqhaXH97V1myxlIVySwWAgNTW1wuEuGzduZNy4ccTFxbFgwQLq1atX5b5KSkrIz8+nYcOGpKSkVLlKtHBPtgTZSOA54DOt9Q6lVDNgpWPLEqL6jEYj69atq3C4y/r165k0aRINExpx01OzMQZG89leI6AZ0CTgvHN/5Tco8vX1pXfv3hgMBjnf56FsuQr8M/CzUipCKRWutT4APOn40oSwXflNzAsKCi5aaHTNmjVMnTqVxMRErn9iFl8dDeZwWQlbT1lubx3o9+fCpuWLlkZHR9O9e3dCQkIwGAy13h5RO2y5CpyMZQpcuOWhOgM8oLXe6OjihLBF+f08Tp06ddFwl59//pnp06fTrHkL+j86k1L/MG5rDikN/GkaUQbos+P9ypevat68OR06dKhwupzwLLYcAr8N/F1r/QuAUupKLIF4yeWwhHA0rTU7d+4kMzPzotVdfvzxR2bNmkXbtm255tHpfHE4AChjSGt/GoT5cEfLP8fwld+no3v37iQkJMghr5ewJQDzy8MPQGv9q1Iq34E1CWGzzMxMdu3addFYv++++46XXnqJtu07MOCRqfRsHAEB5/f44M+FDAIDA+U+HV6o0gBUSnW1fptmnQq3FMuS9ncDqxxfmhBVO378OL/99hsRERHnjfX7+uuvmTdvHl26dKHPw1P4PMOXgOCy83p88OfafXFxcXTr1k2u8nqhqnqA8y54fO4d3eTeHsKpcnJySEtLIzQ09LwbDH3++ecsXLiQrsk9SLl/IsUEcFtzLprXW1paSkFBAa1bt6ZNmzZya0ovVWkAaq3712YhQtiqoKCA1NRU/P39z5uLu3z5chYtWsQVV1xBz/97jk8PKMrP+Z07zKWwsJCysjJ69uxJw4YNndAC4SpsuQociaX319e66WdgmtY615GFCVGR4uJiUlNT0VqfN9Zv6dKlLF68mL59+/LU2An8fAxuSNQE+v7Z+ys/3xcUFMSVV15JRESEs5ohXIQt/f63gXxgiPUrD8tVYCFqVWlpKevWraOoqIjQ0D+Xpfz3v//N4sWL6XNVfzoNG8e/fi/ji/2lRAQq7mgZSHiAwmw2c+bMGerWrUu/fv0k/ARg21Xg5lrru855PFUptdlRBQlREZPJRHp6OmfOnDk71k9rzdtvv817773HddddR8chI1m+1zK4uVM9n4vG97Vq1Yp27drJ+T5xli0BWKSUulJr/SuAUqo3UOTYsoT4k9aarVu3nreun9aaN954g2XLlnHTTTcxatQoDGWKUnMp505vKy4upqSkRMb3iQrZEoCPAP+2ngsEyAH+z3ElCfEnrTW7du3i4MGDZwc6a61ZtGgRn3zyCbfeeisPPPokX+y3jPE7d15vfn4+fn5+9O3bl+joaOc2RLgkW+YCbwE6K6UirI/zHF6VEFYZGRns3Lnz7EBns9nMwoUL+fLLL7n1jrtocsPDvLGthN+zLCOzAv0UNyb6k5ubS1RUFD179iQ4ONjJrRCuyuZlrST4RG07duwYmzdvPjvQ2WQyMW/ePL799luGDRtG3auG8+WBMgDaxfjQMtqHK+r7kJOTQ+PGjUlKSjpvjKAQF5K/HcIlnT59mrS0NMLCwvDz88NkMvHCCy+wYsUKhg8fzl33/B+vbykBLOH3aFIQgZRiMOTToUMHWrZsKef7xCVJAAqXk5ubS2pqKsHBwfj7+1NWVsasWbNYuXIlg+/7K6faD+bVzUXsybFc7X24YxB+pmKKS0tJSUmhQYMGzm6CcBO2DISOBXoDDbFc/d0OpGutzQ6uTXihgoIC1qxZg5+fH4GBgZSWljJ9+nR++eUXet/1EBktb+dk9p9DXR7uGAQlBSh/f/r16yeLGYhqqWoxhP7AOCAG+A04CQQBtwPNlVLLgXlyblDYy4WzPIxGI88//zypqan0vfsRMhNvhkKIDVZ0jfNhYNMATIW5REdH06NHD7nYIaqtqh7gQOBhrfWhC59QSvkBNwPXAp84qDbhRYxGI6mpqRQVFREREUFJSQkTJ04kPT2dR58YSUbCdZBtPnu+L9TPcnPyRo0aycUOcdmqWgzhmSqeKwM+d0hFwuuUlZWRlpZGXl4ekZGRFBUVMWHCBDZv3swTo54ho2F/fj9lPnvIG6TKyM0toF27drRq1UoudojLVumcIKVUT6XUFqVUgVIqVSklt8IUdmc2m9m4cSOnT58mIiKCwsJCxo0bx5YtWxj5zDj2xfVn6ykz7eooHu4YhL+5hMLCQrp3707r1q0l/ESNVDUpchEwBqgDzAderpWKhNfQWrNlyxaOHTtGZGQkBoOBsWPHsn37dkY/O4Fddfrwu/WCR8soP5TRgNlspk+fPiQkJDi5euEJqgpAH631Cq11idb6Y6Dqm6gKUQ3l9/LIyMggKiqK/Px8xowZw+7duxnw0Hh+Cuh5NvzaxfjQI7qI4OBg+vXrJzcnF3ZT1ZnjKKXUnZU91lp/6riyhKfbv3//2Xt55OXl8fToMWRmZtJ08Hh2R/eAYsvr2sX4MCyxmKYN40hOTj5vAVQhaqqqAPwZuKWSxxqQABSXJTMzk23bthEZGcmRUzk8PWoMOSePUe+OiZQ17gZA3SDoFudD75giOrRsSqdOneQ2lcLuqroK/NfaLER4h2PHjrFp0yYIDOPdDSf4aN6zGM+cot5dkwlOTKJuECTX9+W6Rj4oo4EOHTrSokULudghHKKqgdD3AR9UNuNDKdUcaFC+TqAQl3Ly5El+WpPG6lNBpO49yq53x2MynCF2yFRimnagb4IvNzULxN9cQklJEck9ehAfH+/ssoUHq+oQuA7wm1JqI7AROIVlJkgLoB9wGstMESEuKScnh3Xr1vHzSX++3f4HJ5aOx1SUT+Nh02jVti1/bR9MgzAfDAYDJqBPnz5ysUM4XFWHwAuVUq8CV2OZC9wJy1zgncBfKpohIkRF8vLyWLNmDUcKfVmx/Q+Ovz8eXVpI94dnMPKGzjQI80FrTV5eHsHBwfTq1YuwsDBnly28QJXzh7TWJmCF9UuIajMYDKxZs4bMfMWclUf4Y+l4tKmMGXPm0atza8AyJCY3N5c6derQo0cPuUG5qDVVnQP0Ax7EsvhBPJYrv8eAL4C3tNaltVKhcFtFRUWsWbOGY/kmXvrpEMeWTkBpmDRrLr06twQ4e7e2Ro0a0aVLF5nTK2pVVX/b/gOcAaYCR6zbErDcD+Q94G7HlibcWUlJCWvXruVQTgmzf8zg8PsTUb5+TJn9En06NgMsd3rLzc2Vu7UJp6kqALtqrVtfsO0IsE4ptceBNQk3V1paSmpqKgaDgSVrD5Lxn4ko/yAGj3nhbPgZjUYMBgNJSUk0bdpUhrkIp6jqv9wcpdRgpd4FMsAAAB7RSURBVNTZ1yilfJRSd2O5M5wQFykrK2P9+vXk5uaybuch1r0xARUQQo+/zeGelKaAZd2/oqIiUlJSaNasmYSfcJqqeoBDgReA15RS5YEXBay0PifEecxmM+np6ew4fJrXV+5ly7tT8A2JJG7YLLq3iic8QGEwGAAZ5iJcQ1XDYDKwnudTStUBlNb6tK07Vko1Av4N1AfMwGKt9cIaVStcltls5vDhwxw6kc38/+1i7wfT8A2vQ/2hM7m2XSwDmviTl5dHUFAQV1xxhQxzES7BpktuWuuscx8rpa7VWl9qaEwZMFprvUkpFQ5sVEqt0Fr/fpm1ChdVvqxVdnY2X63fxd73p+IXGUf9oTMZ07c+Hev5nl3sNCUlhaCgIGeXLARw+XeFewtoXNULtNZ/AH9Yv89XSu3EMpxGAtCDaK3Zvn07Bw8e5Ie0bSxfOJOAmHi6PjSTJ66oT/1QxZkzZ2jQoAHdunXD39/f2SULcVZV4wC/rOwpLNPkbKaUSgS6AOur8z7h2rTW7Nq1i71795K+ZTv/fGkGAfWaUO/u6ZiDI4kLsUyBa9asGZ06dZJhLsLlVNUD7APcBxRcsF0BPWz9AKVUGJYbJ42s6A5ySqkRwAiA+Ph4MjIyzns+Kyvrwrd4BHdvl9aaU6dOsTvjKK/9dwPr3n+JgLgWxA2ZSmhoGH9paZnd0bBhQyIjIzl0yH1nTrr776oyntiu6rapqgBcBxRqrX++8Aml1G5bdq6U8scSfu9XtoCq1noxsBggOTlZJyYmXvSairZ5Andu14EDB9h18AizP0ll1/L5BDZsQ+zg5wkPDWFaih8+pYV07dqVxo0be8QwF3f+XVXFE9tVnTZVdRX4xiqe63upHSvL3/q3gJ1a6/k2VyRc3qFDh1i6agsLP/mFU98sJLBxB2LvmkxkSDCPdgA/UzE9e/Wifv36zi5ViCo5cuJlb+AvwDal1GbrtvFa628c+JnCwY4ePco3v27k5Y9+Iut/rxKU2IXYOycwumckTYOL8PHxoVcvGeMn3MMlA1AplY9lIYRz5QLpWIa5HKjofdaFUt3/2Eecdfz4cd74ZgOLPv6e7BWvE9wsmfp3jmf8FRHE+RXi4+ND3759CQ8Pd3apQtjElh7gfCyrwHyAJdCGYhncvBt4G7jKUcUJ13Hq1CmWfLueV5f+l5yVbxHcMoWGtz/LtD7hhGkDYWHhJCQkSPgJt2LLuIQbtNZvaK3ztdZ51osWA7XWy4BoB9cnXEBWVhZvfZ3Ki+98Ss7Ktwhp3ZuEO8fxbEoYIaZ8YmJi6N27t4zxE27Hlh6gWSk1BFhufTzonOcuPDQWHiYnJ4cXl6/hjf8sI3fNB4S060fy0FGM6h6KuSiP+PgEWcdPuC1beoD3YrmYcdL69RfgPqVUMPC4A2sTTpabm8uCT3/l9XffJ3fNB4R2GECjW0fxdHIIpsJcmjVrRrdu3ST8hNu65N9c60WOWyp5Wu4I56Hy8/OZs+wXXvvXEvI2fEZY5+tpfPNjPJEUgC7Op3379rRq1cojxvgJ73XJHqBSKkEp9ZlS6qRS6oRS6hOlVEJtFCeco6CggH9+/jOLFi8mb8NnhHe9iaS7HmdBvyDifC0DnCX8hCew5RB4CfAl0BDLYgZfWbcJD2QwGJj30Uqmz1tE/savCO9+O01ufISH2vtSWFhISkoKTZo0kfATHsGWAKyntV6itS6zfr0D1HNwXcIJioqKmLH0R156eSEFW74jImUwDQc8yOQePsT4l9G7d28aNGjg7DKFsBtbAvC0Uuo+pZSv9es+wPNmUXu54uJinnl7Ba+8/DKG7T8S2fse6vQbzmMdIMxf0bdvX+rWrevsMoWwK1su3z0AvAq8jGXYy1rrNuEh1u09wQNvryPzs5co3L2GqL7Die09hGc6m2kSY1nBOTQ01NllCmF3tlwFPgTcWgu1CCf4Izuf+95M5djnL1C0dx3R/R+gwRV38lT7MprHRtCrVy9ZwVl4rKoWRH2FKgY6a62fdEhFotZ8mp7J0x9u4tRnsyg6kE70gL+RMuAW7mlWSmKDuvTo0YOAgABnlymEw1TVA0yvtSpErZv06WbeXbOfU5/OpDjjN2Kuf5zHh91MUkQh8fHxdO3aVQY4C49X1XqA7164TSlVX2t93LElCUf6avNRRn60hdLiIk5+Mo2SQ9uoc+NTDLr5WjqHG0hMbErnzp1l+XrhFar7X/w3QFdHFCIc71+r9zPjm12YSwo5ufx5So7uou7Noxh/77U0CTDQpk0b2rZtK2P8hNeobgDKvww39dXmo5bwKy7gxEdTMB7fS/shzzB9+DVQUkDHjp1o3ry5hJ/wKtUNwDcdUoVwuFHLNmMqyufkR5Mwnszg7icm8eDNvSkoKKBbt240blzlXU6F8EjVCkCt9WuOKkQ4zpyvd1BsyOXEsomUZh3h/lFTGDqg+9mpbTK7Q3gruczn4VbuPM6ib3/jxLIJlJ05TrOhkxhydTeMRiO9e/eW2R3Cq0kAerApn2/lrRW/ceLDiZjyT1Fv0BRG3dQZrTV9+vQhKirK2SUK4VQSgB5qyudbeev7jZxYOgFT4RliB0/l6evb0yLGjyuuuELu3SEEEoAeaeTSdD7+eQsnPpyAubiAuCHTuSm5Fd0aWub1hoSEOLtEIVyCBKCHeXb5b3y86jdOLJ2ALi0ibuhMnuyXSK+m4aSkpMi8XiHOIQHoQZ5d/hvv/W89Jz6cgDaVETdsFo/3aUSflnXo2bOn3LVNiAtIAHqIR/+TxherNnDiw4mgIG7YLMb0a0jv1g3kxkVCVEL+VXiAN1fv44uf1nNi2USUrx9xQ2fyQI84rurQhKSkJJnXK0QlJADd3G3/WEXaxs2c/GgSyj+YuGEzGZZUhyEpLWjfvr2EnxBVkAB0Y0Nf/5W0DRs58dFkfILDqT9sFo8kRzGodztat24t83qFuAQJQDe0KTOHu99IJT9zGyeXT8U3JIq4YbPo1TSCYVd1plmzZhJ+QthAAtANDfnnWvIzt3Dqk2n4htejwdCZLBwQLosaCFFNEoBu5q5Fq8k/uIlTn87AL6o+cUNn8kjXMHr27EHDhg2dXZ4QbkUC0I0Mff1Xfln1E6c+m4l/nUa0vncGM68KIyUlhdjYWGeXJ4TbkQB0Ew8uWcdP33/LqS9eJCA2kdgh05nQJ4wrr7yCmJgYZ5cnhFuSAHQDzy7/jS8//5zTX71EQIOWxA2eykOdw7jx6iuJjIx0dnlCuC0JQBc3bvlm3nr3fbK+fpnA+DbEDprCra1CGTW4H2FhYc4uTwi3JgHowh5asp7PP15K1rf/ILBxR2Lvmsg1iSG8+H/9ZUUXIexAAtBFXT9vJekrPiH7f4sISuxCvTvH06V+MP8ccY2s6CKEnUgAuqDnv9jGhu+WkfPDGwQ3706925+lY70gPn7qWgICApxdnhAeQwLQxazadZKFCxZwZtXbBLdMod5tz9ClfiAfP3mdLGclhJ3JTHkX8o8fdnPHiDGcWfU2IW36UO+2sQxpH8knI2+Q8BPCAaQH6CI+WJfB5ClTyV27lND2/akz8Ekah/vzwvD+sqKLEA4iAegCNmZk8+jTz5C3bjmhHQdQ54bHCA/w5+cJN0r4CeFA8q/LyeZ8vYP+gx8gb91ywpJuoM6Nj1MvxJ9t0wdK+AnhYNIDdKIH30rl49dmkr/pa8K73UL0NQ8REeDLhikDZTkrIWqBw7oYSqm3lVInlVLbHfUZ7uypT/bw0StTyd/0NRE97iT6mocIUr5snSbhJ0RtceQx1jvADQ7cv9t6f+0BfnznRQq2/I+IXncTddX/ERnoy+45N0n4CVGLHBaAWuvVQLaj9u+uHnknlREPP4Bh+49EXnkv0X3vJS7Uj63TbnJ2aUJ4HaefA1RKjQBGAMTHx5ORkXHe81lZWU6oyjFe++UQ/5kznsLda4jq939EptxFTKAPHw1vd1G73ZEn/a7OJe1yH9Vtk9MDUGu9GFgMkJycrBMTEy96TUXb3M2YpWm8Nm0sRfvWE331Q0R0vxV/fNg01bN6fp7wu6qItMt9VKdNTg9Ab/DPFb+zaOLfKT6wkZhrHyG8680EK9g527PCTwh3IwHoYO/8vItRD99LceYWYq5/nPCkG/AHvv1be2eXJoTXc+QwmKVAKtBaKXVEKfWgoz7LVb301SYeGT6E4swt1Bk48mz47Z0jPT8hXIHDeoBa62GO2rc7+Dp9P5Me/Qslx3ZR95bRhLa7CpDwE8KVyCGwA/y8NYM7bx2I8cR+6t46ltA2VwKQIeEnhEuRyaZ2tuCrdK67bgDGEweod/tzEn5CuDAJQDua+fFaxj44GOPpQ8TeOZGQlimAhJ8QrkoOge3k3RWbmProMMpyTxA7aArBiUmAhJ8QrkwC0A6+W7+Dh4beiik/i9jBUwhq3AmQ8BPC1ckhcA09+s9vuOWGAZgKsokdMk3CTwg3Ij3AGvhgRRpvjvsr5hIDcXfPILBha0DCTwh3IQF4mfbu3cvwu25Cl5YQN3QmgfVbABJ+QrgTOQS+DK99uoq2XXqiy4zEDZPwE8JdSQ+wmr75eT1P/OV2UIq4YbMJqNcEkPATwh1JAFbDqrVp3HLjdShfP+KGzsK/TgIAj/Zt6uTKhBCXQwLQRp99/zODbr0JFRhiCb/oBoD0/IRwZxKANlizZg133TIQ3+Bw4obNwi8yDpDwE8LdyUWQS7htwhv06T8A39Ao4u6ZI+EnhAeRHmAVWgyfxYGl0/CNqGfp+YXFABJ+QngKCcBKNLt3GhkfzcQvqgFxQ2fgGxoNSPgJ4UkkACvQ+O7JHP5kNv51GhN393R8QyIBCT8hPI0E4AVa3zOZw8tnERDXjNgh0/ENCgMk/ITwRBKA52hy11gOfTaPwAatiB0yFZ/AUEDCTwhPJQFo1fGe5yzhl9CO2Lsm4xMYAkj4CeHJJACBTkPHsn3ZXIKadKTenZPxCQgCJPyE8HReH4Cx1z/Gqe9fI6hpN+rdMR4f/0BAwk8Ib+DVARgzYAQ5P75JcIse1LvtOZSfPyDhJ4S38NoAjO7/AGdWLSGk1RXUvfUZlK+EnxDexisDMKrvX8j95T1C2val7k2jUL6WH4OEnxDexasCUGtN9JX3kLv2Q0Lb96fOwJEoH19Awk9UX2lpKUeOHKG4uNjZpVyWsrIydu7c6ewy7MpsNlNaWoq/v79Nr/eaANRaE9VrMHnrPyGs03XEXP+YhJ+okSNHjhAeHk5iYiJKKWeXU20lJSUEBgY6uwy70Vpz/Phxjhw5QtOmtq3R6RWrwWitiex+uyX8ugwk5obHJfxEjRUXF1OnTh23DD9PpJQiOjq6Wj1yj+8Bms1mIpNvoeC3bwjvdivR1zx89i+shJ+oKQk/11Ld34dHB6DZbCayy40UbP2eiJ53EdXvfgk/IcRZHnsIbDKZiOh8LQVbvyfyiqFnw69heICEnxAC8NAALC0tJaLj1Ri2/0TklfcS1ec+lFIMaF2XtROudXZ5QtjVtm3baNKkCf/85z9r7TO/++47WrduTYsWLZgzZ06Fr1m4cCEdOnSgffv2LFiw4LznXn75Zdq3b0+HDh0YNmzY2fN2Z86cYdCgQbRp04a2bduSmppa5fYa01q7zFe3bt30hQ4ePHjRtqpM/HijDml1hQZ01FX36ybP/lc3efa/et/J/Grtx9Gq2y534Ilt0rrydv3++++1W0gV1q5dq1NSUqr1nuLi4sv6rLKyMt2sWTO9f/9+XVJSojt16qR37Nhx3mu2bdum27dvrw0Ggy4tLdXXXHON3rNnj9Za6yNHjujExERdWFiotdZ68ODBesmSJVprrYcPH67ffPNNrbXWJSUlOicnp8rtFbWpot8LkK4ryByP6gGu23Ocec89SuGetURf/TCRPQcB8M793WleL8zJ1QnhOLGxsezYsaNWPistLY0WLVrQrFkzAgICGDp0KF988cV5r9m5cycpKSmEhITg5+dHv379+Oyzz84+X1ZWRlFREWVlZRQWFtKwYUPy8vJYvXo1Dz74IAABAQFERUVVut0ePCYAB8z5jv433EzRvjRirvs7Ed1vA+DH0f24qk2sk6sTwrHGjRtHSUkJmZmZl72PPn36kJSUdNHXDz/8cN7rjh49SqNGjc4+TkhI4OjRo+e9pkOHDqxevZqsrCwKCwv55ptvOHz4MADx8fGMGTOGxo0b06BBAyIjI7nuuus4cOAA9erV469//StdunThoYcewmAwVLrdHjziKnDjUZ9w6tPpFGduJeaGJwnvfB0gV3qF68k2GPk4/TCDkxsRExpgl31+9913GAwGbrrpJnbs2EGTJk0A+OCDD/juu++IjIzEYDDw5ptvsnz5ctasWYPZbCYoKIi5c+ee3c8vv/xi0+dZjijPd+Hwk7Zt2/Lss89y7bXXEhYWRufOnfHzs8RNTk4OX3zxBQcPHiQqKorBgwfz3nvv0aZNGzZt2sQrr7xCz549eeqpp5gzZw633XZbhdunT59+uT+ys9w+ABs//TEnP5lGyZHfqXPT04R1uBqQ8BOu6eP0w8z+dhcAf+vXvMb7Ky4uZuzYsXz55ZcsWbKE7du3M3DgQH799VdWrVrFv//9b8ByyJmWlsb69ev5xz/+AUB+fv55++rTp89F2wDmzp3LgAEDzj5OSEg425sDy4yYhg0bXvS+Bx988Oxh6/jx40lISADghx9+oGnTptSrVw+AO++8k7Vr1zJgwAASEhLo2bMnAIMGDWLOnDk89thjFW63B7cOwMZPf8TJj6ZQ8sce6t48mtB2/QAJP+G6Bic3Ou/PmpoxYwbDhw8nMTGRjh078uWXXwLw7rvvMmbMGLTWjBkzhry8PHx8fJgwYcLZ9wYEnN8DtbUH2L17d/bu3cvBgweJj4/nww8/5IMPPrjodSdPniQ2NpZDhw7x6aefnr1y27hxY9atW0dhYSHBwcH8+OOPJCcnU79+fRo1asTu3btp3bo1P/74I+3atat0uz24bQA2GrmMkx9NwnjiAHVve5bQ1r0BCT/h2mJCA+zS8wPYvXs3K1asYM2aNQB07NiRWbNmAZZJAP7+/iilmDJlCiNHjsRoNJ49DAXLWNnL4efnx6uvvsr111+PyWTigQceoH379gAMHDiQf/3rXzRs2JC77rqLrKws/P39WbRoEdHRllvL9uzZk0GDBtG1a1f8/Pzo0qULI0aMAOCVV17h3nvvxWg00qxZM5YsWVLl9ppSFR3PO0tycrJOT08/b1tGRgaJiYnnbWv05Aec/GgyxtOZ1Lv9OUJaWLrG7hR+FbXL3Xlim6Dydu3cuZO2bdvWfkE22LlzJ1OnTqVBgwYUFhYyaNAgGjZsyIwZM6hXrx75+fnMmTOHuLg4Z5dqVyUlJRw4cOCi34tSaqPWOvnC17tdD7DRE+9zYtlESrOPEnvnJIKbdQPcK/yEcLS2bdvy4YcfXrR96dKlZ78vKSmpzZJcklsFYMLj/+HkhxMoyz1J7KApBCcmARJ+QojL4zYBmPD3dznx4XhMBdnEDn6eoMYdAQk/IcTlc+hAaKXUDUqp3UqpfUqpcZe7n4RHl3Bi6ThMhjPEDpku4SeEsAuHBaBSyhdYBNwItAOGKaWqfe269+wfOP7Bs5iL8okbOoOgBMvJTQk/IURNObIH2APYp7U+oLU2Ah8Ct1VnB/EPv8GJD8ahjcXEDZtFYINWgISfcB2uNIpCVP/34cgAjAcOn/P4iHWbTcrKyjj5yVS0uYy4e2YTEGcZOyXhJ1xFUFAQWVlZEoIuQmtNTk4OQUFBNr/HkRdBKlqb+qK/KUqpEcAIsEySzsjIOPtcnRufwic4goC6jQFY9Uj78553Z1lZWc4uwe48sU1Qebu01pw4cYI//vijliuyD7PZjI+Px6yHAlg6TpGRkTbnhCMD8Ahw7nyfBODYhS/SWi8GFoNlIPS5A07rNOuAoRRC/WHHdM/r+XnioGFPbBN4Zrs8ceB6ddvkyADcALRUSjUFjgJDgXuqs4Md02/yyF+SEMI1OCwAtdZlSqnHgf8BvsDbWuvaWbFRCCFs4NCB0Frrb4BvHPkZQghxuVxqMQSl1CngwiVt6wKnnVCOo3liuzyxTSDtcieVtamJ1rrehRtdKgAropRKr2gVB3fnie3yxDaBtMudVLdNnnUNXAghqkECUAjhtdwhABc7uwAH8cR2eWKbQNrlTqrVJpc/ByiEEI7iDj1AIYRwCJcNQHutJehqlFJvK6VOKqW2O7sWe1FKNVJKrVRK7VRK7VBKPeXsmuxBKRWklEpTSm2xtmuqs2uyF6WUr1LqN6XUf51di70opTKUUtuUUpuVUumXfoeLHgJb1xLcA1yLZU7xBmCY1vp3pxZmB0qpvkAB8G+tdQdn12MPSqkGQAOt9SalVDiwEbjd3X9fynK371CtdYFSyh/4FXhKa73OyaXVmFJqFJAMRGitb3Z2PfaglMoAkrXWNo9tdNUeYI3XEnRVWuvVQLaz67AnrfUfWutN1u/zgZ1UY+kzV6UtCqwP/a1frtdjqCalVAJwE/AvZ9fibK4agDVaS1A4j1IqEegCrHduJfZhPVTcDJwEVmitPaFdC4CxgNnZhdiZBr5XSm20LrN3Sa4agDatJShci1IqDPgEGKm1znN2PfagtTZprZOwLOfWQynl1qctlFI3Aye11hudXYsD9NZad8VyG47HrKebquSqAWjTWoLCdVjPkX0CvK+1/tTZ9dib1voMsAq4wcml1FRv4Fbr+bIPgauVUu85tyT70Fofs/55EvgMy6m0KrlqAJ5dS1ApFYBlLcEvnVyTqIT1YsFbwE6t9Xxn12MvSql6Sqko6/fBwABgl3Orqhmt9XNa6wStdSKWf1c/aa3vc3JZNaaUCrVegEMpFQpcB1xypIVLBqDWugwoX0twJ/CRp6wlqJRaCqQCrZVSR5RSDzq7JjvoDfwFS29is/VroLOLsoMGwEql1FYs/ymv0Fp7zLARDxMH/KqU2gKkAV9rrb+71JtcchiMEELUBpfsAQohRG2QABRCeC0JQCGE15IAFEJ4LQlAIYTXkgAUZyml7ldKvVqD93+nlDpz4Qoj1vGc65VSe5VSy6xjO8ufG6mUGm79/h2l1EHrMJpNSqle1u1zlVJXX25djmCtO6Sa77nKk1Zf8QQSgMKeXsIyHvBCLwAva61bAjnAgwBKKT/gAeCDc177jHXq2TjgDeu2V6yPXclIoFoBKFyPBKCokFKqiVLqR6XUVuufja3bmyul1imlNiilpimlyldLQWv9I5B/wX4UcDWw3LrpXeB26/dXA5usA98vtBpoYd1vJlBHKVW/gjqbW3ueG5VSvyil2li3f3FOz/JvSqn3rd+vUkotUEqtVUptV0r1sG4Pta7VuMG6Tt5t1u2+1h7oNuvP4gml1JNAQyyDpFdaX3edUirV2nP92Dovunxdy11KqV+BO6v1SxAOJwEoKvMqljULOwHvA/+wbl8ILNRad8e2+dl1gDPnhNy5K/v0xrJ2YEVuAbad83iT9fUXWgw8obXuBowBXrNuHwFMVkr1AUYDT5zznlCt9RXA34G3rdsmYJkW1h3oD7xknVI1AmgKdCn/WWit/4Gl7f211v2VUnWBicAA62T8dGCUUioIeNPalj7ARQEunMvP2QUIl9WLP3ss/wFePGd7eQ/uA2DuJfZT1co+DbBMdTzXS0qpicAprIfKViex9Lr+3LGll3UF8LGlowlAIIDW+oRSajKwErhDa33uGoxLra9ZrZSKsM73vQ7LIgFjrK8JAhpjmf/7enmAX7CfcilAO2CNtY4ALNMd2wAHtdZ7rfW+hyVQhYuQAPRiSqnHgIetDy81d/dy50yeBqKUUn7WEDl3ZZ8iLEFzrme01su5WJD19efywdK7TKrkszsCWVwQnFzcFo0lqO/SWu8+9wnrIfyl2q6wzBMedsF7k2x4r3AiOQT2YlrrRVrrJOvXhYeza7GsFgJwL5bl4AHWAXdZvx/KJWjLZPOVwCDrpv8DvrB+vxPreT4btOKC1T2saw4eVEoNBktYKaU6W7/vgWVduC7AGKVU03Peerf1NVcCuVrrXCwLbzxhDTyUUl2sr/0eeMR6wQalVIx1ez4Qbv1+HdBbKdXC+poQpVQrLCvHNFVKNbe+7ryAFM4nASgq8yTwV+tKKH8Bym90NBLL+a00LIewueVvUEr9AnwMXGNd6eZ661PPWt+zD8s5wbes278FLrlopbKsNdgCy7m1C90LPGhdBWQHcJtSKhDLubcHrME+Gni7PNyAHKXUWuB1/jzMno5lyfutynLDqunW7f8CDlm3bwHusW5fDHyrlFqptT4F3A8stf681gFttNbFWA55v7ZeBMm8VFtF7ZLVYES1WMe+FWmttVJqKJabVV32/VqUUp8BY8vPk1XymjuArlrrSZf7OefsaxUwRmtt013DhGeTc4CiuroBr1p7U2ewjOOriXFYepKVBiCWv6fzavg5QlxEeoBCCK8l5wCFEF5LAlAI4bUkAIUQXksCUAjhtSQAhRBeSwJQCOG1/h9xqjlo9F8uYgAAAABJRU5ErkJggg=="
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}